Richang Hong

Joint Item Recommendation and Attribute Inference

2020

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item …

RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding

2020 · arXiv (Cornell University)

Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recommender tasks and have …

REDGCN: Rating-Oriented Explicit Disentangling Graph Convolution Network for Review-Aware Recommendation

2024 · IEEE Transactions on Computational Social Systems

Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. …

Deep Item-based Collaborative Filtering for Top-N Recommendation

2019 · ACM Transactions on Information Systems

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that …

A Neural Influence Diffusion Model for Social Recommendation

2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the …

MMGCN

2019

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well …

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

2020

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. …

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction …

Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

2021

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors …

A Review-aware Graph Contrastive Learning Framework for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing …